Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

Westerhuis, Johan A.; Velzen, Ewoud J. J. van; Hoefsloot, Huub C. J.; Smilde, Age K.

doi:10.1007/s11306-008-0126-2

Cited by 77 publications

(68 citation statements)

References 7 publications

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“…The statistical signifi cance of the classifi cation PLSDA models was assessed using permutation testing with 1,000 permutations ( 37 ). Q 2 was used as quality-of-fi t criterion for the permutation test ( 38 ). Further details are given in supplementary Methods.…”

Section: Discussionmentioning

confidence: 99%

Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease

Reis

Rudnitskaya

Chariyavilaskul

et al. 2015

Journal of Lipid Research

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Chronic kidney disease (CKD) is a serious and increasingly common condition ( 1 ). Patients with CKD have a greatly increased risk of CVD, which represents the most common cause of mortality and morbidity in these patients, to the extent that CKD is considered an independent risk factor for CVD ( 2, 3 ). In CKD, many conventional risk factors for CVD are prevalent, including hypertension, dyslipidemia, and insulin resistance. Underlying conditions that are typical of CVD also occur, such as heightened infl ammatory status, oxidative stress, endothelial dysfunction, and arterial stiffness ( 3, 4 ). Consequently, understanding the factors in CKD that could contribute to increased CVD risk is very important.In CVD there is a clearly established link between dyslipidemia (specifi cally hypercholesterolemia and hypertriglyceridemia) and atherosclerosis, an underlying pathology of most CVD ( 5, 6 ). In view of the clear cardiorenal relationship, there has been considerable interest in the possible contribution of hyperlipidemia to CKD-associated CVD ( 7,8 )

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Section: Discussionmentioning

confidence: 99%

Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease

Reis

Rudnitskaya

Chariyavilaskul

et al. 2015

Journal of Lipid Research

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“…47 The predictability was measured using Q2, which is defined as 1 minus the ratio of the prediction error sum of squares (PRESS) to the (mean corrected) total sum of squares (TSS) of the response Y. [47][48][49][50] Values of R2Y and Q2 close to 1.0 indicate a better model. 50,51 We validated the PLS-DA model through permutation analysis (800 times) to reduce possible false-positive findings using SIMCA-P 11.5 (Umetrics AB, UMEA).…”

Section: Data Preprocessing and Analysismentioning

confidence: 99%

Potential serum biomarkers from a metabolomics study of autism

Wang

Liang

Wang

et al. 2016

jpn

104

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“…Whereas univariate approaches neglect covariance effects and suffer from multiple testing issues, the multivariate approaches are fraught by the risk of overfitting (95). Hence, rigorous crossvalidation approaches have been proposed (96). Multilevel approaches have been shown to be capable of distinguishing subtle polyphenol-induced effects in the presence of large phenotypic variation between subjects (31, 97).…”

Section: Metabolomicsmentioning

confidence: 99%

Metabolic fate of polyphenols in the human superorganism

Duynhoven

Vaughan

Jacobs

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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465

330

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Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations.polyphenol bioconversion | gut microbiota | metabolomics | metagenomics | microbiomics

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Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

Cited by 77 publications

References 7 publications

Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease

Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease

Potential serum biomarkers from a metabolomics study of autism

Metabolic fate of polyphenols in the human superorganism

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